One thing you won't hear him championing is the unfettered rise of artificial intelligence, which he once described as the "biggest existential threat" to humankind. Musk's prejudice prompted him to donate millions to the ethics think tank OpenAI--and it's why he's urging other billionaire techies like Facebook's Mark Zuckerberg and Alphabet's Larry Page to proceed with caution on their myriad of machine learning and robotics experiments. OpenAI is both an ethics and a research institution. Its mandate (plucked from its website): "Because of AI's surprising history, it's hard to predict when human-level AI might come within reach. When it does, it'll be important to have a leading research institution which can prioritize a good outcome for all over its own self-interest."

A basic – yet very successful – tool for modeling human language has been a new generation of distributed word representations: neural word embeddings. However, beyond just word meanings, we need to understand the meanings of larger pieces of text and the relationships between pieces of text, like questions and answers. Two requirements for that are good ways to understand the structure of human language utterances and ways to compose their meanings. Deep learning methods can help for both tasks. I will then look at methods for understanding the relationships between pieces of text, for tasks such as natural language inference, question answering, and machine translation.

Hedge fund Renaissance Technologies is looked upon by Wall Street with awe and envy in equal measure. Particularly, Medallion Fund, an employees only fund it runs. Bloomberg last year wrote the fund has returned more than $55 billion, making it more profitable than funds run by feted veterans such as George Soros. The Renaissance flagship fund, which will turn 30 next year, has returned more than 25% profits in most of its years of investing. Money doubles in a little more than three years at that rate. Medallion turned in 40% or thereabouts in 13 of its years; at that clip, money almost doubles in just two years.

Hedge funds have been trying to teach computers to think like traders for years. Now, after many false dawns, an artificial intelligence technology called deep learning that loosely mimics the neurons in our brains is holding out promise for firms. WorldQuant is using it for small-scale trading, said a person with knowledge of the firm. Man AHL may soon begin betting with it too. Winton and Two Sigma are also getting into the brain game.

Feel free to forward this email or share it with your network. How Google used artificial intelligence to transform Google Translate, one of its more popular services and how machine learning is poised to reinvent computing itself. Zuckerberg's dead-eyed delivery during a two-minute humblebrag about his artificial intelligence tool Jarvis makes you question who the real robot is. DEEPMIND HAS SURPASSED the human mind on the Go board. Watson has crushed America's trivia gods on Jeopardy.

Elon Musk is a well-known harbinger of the potential for ill held by artificial intelligence. The Tesla and SpaceX CEO also helped start OpenAI, a group with a broad mandate that focuses on developing AI out (as the name implies) in the open, rather than behind closed doors as the exclusive province of high-powered governments and secretive private contractors. Musk, it turns out, was in on the AI train early with an investment in DeepMind, which was later acquired by Google. Musk wasn't in DeepMind for a return, as is the case with most investments; he wanted access to greater insight regarding DeepMind's progress, and the progress of AI in general, according to a new feature in Vanity Fair. The enterprising CEO wanted to be able to see how fast AI was improving, and what he found was a rate of gains that he hadn't expected, and that he thought most people would not possibly expect. This was the insight that Musk needed to begin a campaign warning against the potential dangers of AI, and to develop his own efforts to responsibility develop the tech via OpenAI.

In 2014 I optimistically wrote for the Knight Foundation blog that libraries could reinvent themselves in the digital era, tracing my own collaborations with the Internet Archive over the prior year and drawing from my opening keynote address to the 2012 IIPC General Assembly at the Library of Congress. Yet, reflecting back three years later, looking at just how adrift and leaderless so many research libraries have become in the digital era, unsure of how to reinvent themselves and often too arrogant and insular to reach out beyond the communities they have worked with for centuries, I am no longer so certain that research libraries and the academic communities that work most closely with them can genuinely reimagine themselves on their own. Community libraries have found great success reinventing themselves to better fit into modern lifestyles, from collaborative spaces to free wifi to ebooks to and even 3D printers and virtual reality systems, but research libraries as a whole seem to be struggling to find their footing in the digital era. What might they learn from the world of the commercial cloud and indeed the broader technological future of Silicon Valley? The commercial cloud has truly transformed how we think about computing in the modern era, from the shift from hardware to services and experts, the rise of seamless security and unimaginable deep learning systems accessible by a single API call.

Even more amazing, the researchers never explicitly programmed this AI communication. Instead, it "evolved" as a response to a reinforcement learning problem. While the jargon can get a bit technical, the OpenAI blog does a decent job of parsing it. The important thing to grok is the language was never defined, but rather hit upon as a solution to a general problem of learning to communicate. This type of AI method is called reinforcement learning, and involves the use of a reward signal to continually guide the agent towards an optimum outcome.

We propose a second-order (Hessian or Hessian-free) based optimization method for variational inference inspired by Gaussian backpropagation, and argue that quasi-Newton optimization can be developed as well. This is accomplished by generalizing the gradient computation in stochastic backpropagation via a reparameterization trick with lower complexity. As an illustrative example, we apply this approach to the problems of Bayesian logistic regression and variational auto-encoder (VAE). Additionally, we compute bounds on the estimator variance of intractable expectations for the family of Lipschitz continuous function. Our method is practical, scalable and model free. We demonstrate our method on several real-world datasets and provide comparisons with other stochastic gradient methods to show substantial enhancement in convergence rates.

Stochastic gradient descent based algorithms are typically used as the general optimization tools for most deep learning models. A Restricted Boltzmann Machine (RBM) is a probabilistic generative model that can be stacked to construct deep architectures. For RBM with Bernoulli inputs, non-Euclidean algorithm such as stochastic spectral descent (SSD) has been specifically designed to speed up the convergence with improved use of the gradient estimation by sampling methods. However, the existing algorithm and corresponding theoretical justification depend on the assumption that the possible configurations of inputs are finite, like binary variables. The purpose of this paper is to generalize SSD for Gaussian RBM being capable of mod- eling continuous data, regardless of the previous assumption. We propose the gradient descent methods in non-Euclidean space of parameters, via de- riving the upper bounds of logarithmic partition function for RBMs based on Schatten-infinity norm. We empirically show that the advantage and improvement of SSD over stochastic gradient descent (SGD).